Spam detection using hybrid Artificial Neural Network and Genetic algorithm

Spam detection is one of the major problems which considered by many researchers by different developed strategies. Artificial Neural Network (ANN) is one of many others being proposed. However designing an ANN is a difficult task as it requires setting of ANN structure and tuning of some complex pa...

Full description

Saved in:
Bibliographic Details
Published inInternational Conference on Intelligent Systems Design and Applications pp. 336 - 340
Main Authors Arram, Anas, Mousa, Hisham, Zainal, Anzida
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2013
Subjects
Accuracy
Artificial Neural Network (ANN)
Artificial neural networks
Back Propagation (BP)
Biological cells
Genetic Algorithm (GA)
Postal services
Spam
Testing
Online AccessGet full text
ISSN2164-7143
DOI10.1109/ISDA.2013.6920760

Cover

More Information
Summary:Spam detection is one of the major problems which considered by many researchers by different developed strategies. Artificial Neural Network (ANN) is one of many others being proposed. However designing an ANN is a difficult task as it requires setting of ANN structure and tuning of some complex parameters. In this study, ANN was hybridized with Genetic algorithm (GA) in order to optimize the performance of ANN for spam detection. GA was used to determine some ANN parameters and suggest optimum weights to efficiently enhance the ANN learning. Experimental results show that the hybrid ANN and GA has superior performance when compared to conventional ANN.
ISSN:2164-7143
DOI:10.1109/ISDA.2013.6920760